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Modeling the relationship between search terms in clinical queries.
Roni F Zeiger1, Christopher D Stave, Florian Schmitzberger
1VA Palo Alto Health Care System, Palo Alto, CA, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|June 17, 2006
Summary
We developed search hedges to improve clinical query precision in MEDLINE and Google searches. While diagnostic queries showed improved mean average precision (MAP), treatment queries did not, highlighting limitations in current search resource modeling.
Area of Science:
- Medical Informatics
- Information Retrieval
Background:
- Clinical queries require precise search strategies for effective information retrieval.
- Existing search engines like MEDLINE and Google may not fully capture the nuanced relationships between clinical search terms (e.g., diagnosis, treatment).
Purpose of the Study:
- To design and evaluate search hedges aimed at explicitly modeling relationships between terms in clinical queries.
- To assess the impact of these hedges on the performance of diagnostic and treatment-related searches.
Main Methods:
- Development of specialized search hedges for clinical queries.
- Testing hedges on MEDLINE and Google search platforms.
- Pilot evaluation using mean average precision (MAP) as a performance metric.
Main Results:
- A pilot evaluation indicated an improvement in MAP for precomputed diagnostic queries.
- MAP did not show improvement for precomputed treatment queries using the designed hedges.
- The study identified limitations in target resources that do not explicitly model term relationships.
Conclusions:
- Search hedges show potential for enhancing diagnostic clinical query precision.
- Further development is needed to address limitations for treatment-related queries.
- The explicit modeling of term relationships in search resources remains a critical challenge.